Computer implemented system and method for wi-fi based indoor localization

ABSTRACT

The present disclosure envisages a computer implemented system and method for Wi-Fi based indoor localization. The system includes a repository for storing attributes of the floor plan of an indoor area with respect to the zones on the floor plan. A communicating module receives a threshold number of data points from user devices located in the area. These data points include a plurality of Received Signal Strength Indicators (RSSI) captured from the access points positioned in the area. A k-means clustering is then performed on the data points for grouping the data points into ‘k’ number of clusters and a decision tree is built by following a condition based approach. Distance values are then calculated pertaining to the RSSIs stored at the decision tree, and zone circles are plotted. Zone of user presence is then determined by correlating the plotted zone circles upon the floor plan using maximum overlap property.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Indian Priority PatentApplication No. 776/MUM/2014 filed Mar. 7, 2014, the entire contents ofwhich are incorporated herein by reference.

FIELD OF THE DISCLOSURE The present disclosure relates to the field ofindoor localization. DEFINITIONS

The expression “k-means clustering technique” used hereinafter in thisdisclosure refers to, but is not limited to, a vector quantizationtechnique used in signal processing. “k-means clustering technique”groups ‘n’ observations into ‘k’ clusters, in which each observationbelongs to the cluster with the nearest mean.

The expression “Received Signal Strength Indicator (RSSI)” usedhereinafter in this disclosure refers to, but is not limited to, themeasure of the power level that a Radio Frequency (RF) device, such as aWi-Fi or a 3G client, receives from a radio infrastructure at a givenlocation and time.

The expression “condition based approach” used hereinafter in thisdisclosure refers to, but is not limited to, a method of segregatingdata including a set of values. The method involves classifying data inaccordance with a reference value. For example, comparing if each valueamongst a set of values is greater than or less than a reference value.

The expression “Access Point (A.P.)” used hereinafter in this disclosurerefers to, but is not limited to, wireless devices or Wi-Fi sourcesinstalled in an infrastructure for providing network connectivity.

The expression “zone circles” used hereinafter in this disclosure refersto, but is not limited to, an area contained inside a circle that isplotted by considering an Access Point (A.P.) as the center of thecircle, and the distance calculated from RSSI value as the radius of thecircle.

The expression “maximum overlap” used hereinafter in this disclosurerefers to, but is not limited to, the maximum percentage of overlapbetween the area covered by two figures, surfaces or bodies. In thepresent case, it refers to the percentage of overlap between the areacovered by the plotted zone circles and the area covered by the zones ofa floor plan.

The expression “Geometry module” used hereinafter in this disclosurerefers to, but is not limited to, a computer module performing theoperation of maximum overlap.

The expression “Attenuation module” used hereinafter in this disclosurerefers to, but is not limited to, a computer module that takes intoaccount the attenuation or the loss of power of a received signal, andaccordingly compensates the received signal strength values.

BACKGROUND

Wi-Fi based indoor localization systems have fostered a growing interestand gained ample attention from researchers lately because of theubiquitous and inexpensive nature of the required infrastructure.Different technologies are implied with a varying mix of accuracy,stability and challenges such as signal propagation models withtrilateration and location fingerprint. Within a room, the signalpropagation model works fairly. However due to complicated environmentindoor settings and the random effects of signal propagation, it isextremely difficult to build an effective general model of signalpropagation that coincides with the real world situation. For Wi-Fifingerprinting, fine-grained supervised training is normally required toachieve high accuracy and resolution. The database generation,supervised training and deployment requirements are some disadvantagesof location fingerprint.

Further, RF based indoor localization solutions require extensivetraining for fingerprinting, as all of them are supervised machinelearning based approaches.

OBJECTS

Some of the objects of the present disclosure aimed to ameliorate one ormore problems of the prior art or to at least provide a usefulalternative are described herein below:

An object of the present disclosure is to provide an indoor localizationsystem that works without supervision or explicit training.

Another object of the present disclosure is to provide an indoorlocalization system that is self-learning.

Still another object of the present disclosure is to provide an indoorlocalization system that does not involve a trilateration technique.

Yet another object of the present disclosure is to provide an indoorlocalization system that is robust and accurate.

SUMMARY

A computer implemented system and method for indoor localization isdisclosed in accordance with an embodiment of the present disclosure. Inthe current disclosure, a model driven system is used to eliminate thetraining phase and make the system scalable to new environments as wellas accepting changes in existing environments. Using model drivenextrapolation, the system can also achieve localization in regions,which cannot be triangulated, as trilateration base approach requiresWi-Fi access points to be placed in specific geometric positions.

The computer implemented system of present disclosure includes arepository, a communicating module, a clustering module, a decisionmodule, a distance-estimating module, and a geometry module. Therepository stores at least one attribute selected from the groupconsisting of a floor plan of an indoor area including zone details,zone boundaries, building materials and location of different accesspoints with respect to the zones on the floor plan. The communicatingmodule receives a threshold number of data points from at least one userdevice located in the area, wherein each of the data point includes aplurality of Received Signal Strength Indicators (RSSI) captured fromthe access points positioned in the area. The clustering modulecooperates with the communicating module and performs a k-meansclustering technique on the data points for grouping the data pointsinto ‘k’ number of clusters. The decision module cooperates with theclustering module and builds at least one decision tree using the RSSIsof each point in the cluster of data points, and the decision tree isbuilt by following a condition based approach. These conditions arerelated to the values of the RSSIs related to each of the access points.The distance-estimating module that cooperates with the decision module,calculates distance values pertaining to the RSSIs stored at thedecision tree, and plots zone circles. These zone circles are plottedusing the distance values as radii of the zone circles and the accesspoints as centers of the respective zone circles. The geometry modulecooperates with the distance-estimating module and the repository todetermine the zone of user presence by correlating the plotted zonecircles upon the floor plan using maximum overlap property. The geometrymodule is further connected to the communicating module for transferringthe information regarding the zone of user presence.

A computer implemented method for indoor localization is also disclosedin accordance with an embodiment of the present disclosure. The methodcomprises the following:

-   -   storing, in a repository, the floor plan of an indoor area        including zone details, zone boundaries, building materials and        location of different access points with respect to the zones on        the floor plan;    -   collecting a threshold number of data points from at least one        user device located in the area, wherein each of the data points        includes a plurality of Received Signal Strength Indicators        (RSSI) captured from the access points positioned in the area;    -   performing a k-means clustering technique on the data points for        grouping the data points into ‘k’ number of clusters;    -   building at least one decision tree using the RSSIs contained by        the cluster of data points, wherein the decision tree is built        by following a condition based approach, and wherein the        conditions are related to the values of the RSSIs related to        each of the access points;    -   calculating distance values pertaining to the RSSIs stored at        the decision tree and plotting zone circles, wherein the zone        circles are plotted using the distance values as radii of the        zone circles, the access points being as centers of the zone        circles; and    -   determining the zone of user presence by correlating the plotted        zone circles upon the floor plan using maximum overlap property.

BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS

The computer implemented system and method for Wi-Fi based indoorlocalization will now be described with the help of accompanyingdrawings, in which:

FIG. 1 illustrates a block schematic of the computer implemented Wi-Fibased indoor localization system, in accordance with the presentinvention;

FIG. 2 illustrates a sample decision-tree, in accordance with thepresent invention;

FIG. 3 illustrates the process of mapping of zone circles to physicalzones, in accordance with the present invention;

FIG. 4 illustrates a flow diagram showing the steps involved during theprocess of Wi-Fi based indoor localization, in accordance with thepresent invention; and

FIG. 5 illustrates the modes of operation of the computer implementedWi-Fi based indoor localization system.

DETAILED DESCRIPTION

The invention will now be described with reference to the embodimentsshown in the accompanying drawings. The embodiments do not limit thescope and ambit of the invention. The description relates purely to theexemplary preferred embodiments of the invention and its suggestedapplication.

Referring the accompanying drawing, FIG. 1 shows a block schematic ofthe computer implemented system for Wi-Fi based indoor localization(100). The system (100) includes a user device (80) and a server (50).The user device (80) present with the user is connected to the remoteserver (50) via a network. In one embodiment, the network is a wirelessnetwork. However, the network may be a wired network or a combination ofthe wired and the wireless network.

In accordance with an embodiment of the present invention, aninfrastructure comprises of various access points installed therein atdifferent locations. Generally, these access points are Wi-Fi sourceslike routers positioned for providing network connectivity within thepremises of the infrastructure. The user device (80) enabled with Wi-Fimeasures strength of signals coming from different access points. Thesignal strengths received from various access points differ in magnitudebecause of the distance of the access points with respect to the userdevice (80). Further, the received signal strengths are also affecteddue to any obstruction material present on a floor of theinfrastructure. These obstruction materials includes the walls orboundaries made of different construction materials like cement, gypsum,glass, wood. A piece of furniture present on the floor may also provideobstruction to the signal traveling through it. Each floor of theinfrastructure is divided into areas called as ‘zones’.

The signal strength values recorded by the user device (80) are known asReceived Signal Strength Indicator (RSSI). The user device (80) presentwith the user traveling through different zones on the floor at anypoint of time, captures a set of RSSI values from different accesspoints. The set of RSSI values received from different access points iscalled as a data point. The user device (80) identifies each accesspoint using the machine Identification code (MAC ID) values receivedalong with the RSSI values.

During an initial mode of operation, the user device (80) is kept inscanning mode in order to collect data point values at differentlocations while passing through different zones of a floor. The userdevice (80) includes a Wi-Fi module (6) connected to a processor (8).The Wi-Fi module (6) is adapted to capture data point values while theuser moves across the infrastructure. The processor (8) at periodicintervals, transmits the captured data point values to the remote server(50) communicating with the user device (80). The user device (80)further includes a user memory (4) connected to the processor (8).

In accordance with an embodiment of the present invention, the server(50) comprises a communicating module (10) which receives the data pointvalues transmitted by the user device (80). The communicating module(10) is in connection with a clustering module (12). Upon receiving aminimum number of data point values, the communicating module (10)provides the received set of data point values to the clustering module(12). The clustering module (12) employs a vector quantization techniquelike k-means clustering on the received data point values. The value of‘k’ is set to the number of zones present on the floor. The k-meansclustering method employed by the clustering module creates ‘k’ numberof clusters and places each of the data point into one of the ‘k’clusters depending on the cluster with the nearest mean. For example,there are ‘x’ zones on a floor. k-means clustering method is applied tocluster the data points into ‘x’ distinct cluster. Each data point istagged with one of the clusters in this process. After the process ofcluster formation by the clustering module (12), the clustered datapoint values are transferred to a decision module (14) connected to theclustering module (12).

The decision module (14) receives the clustered data point valuesprovided by the clustering module (12). The data points are organizedbased on the RSSI values of each point in the cluster of data points. Bycontinuous organization of the data points by the decision module (14),a branched data structure is created. This branched data structure isreferred to as the decision tree. The decision tree represents anif-else ladder structure and each leaf node of the branched treerepresents a cluster.

Referring to FIG. 1 and FIG. 2, a sample decision tree in accordancewith the present disclosure is shown in FIG. 2. RSSI value of eachaccess point measured in a cluster is organized. The branched datastructure shows first level of organization of access points based onthe RSSI value. First level of organization includes categorization ofthe access point AP2 based on the RSSI value less than or equal to(<=)−55, and RSSI value greater than (>)−55. Further, the second levelof organization involves categorization of the access point AP2 based onthe RSSI value less than or equal to (<=)−55 into RSSI values less thanor equal to (<=)−75 and RSSI value greater than (>)−75. The process oforganization goes on in the described manner until not all the RSSIvalues corresponding to the access points of a cluster are categorized.

The decision module (14) upon development of a decision tree, transfersthe union of values to a distance-estimating module (16) connected tothe decision module (14). The distance-estimating module (16) receivesthe union of RSSI values and converts the RSSI values into distancesusing the below mentioned free-space propagation formula,

d=e ^((RSSI—Unit RSSI)/10n)   (1)

where, RSSI=RSSI at distance ‘d’, and

-   -   Unit RSSI=RSSI at unit distance (measured)

The optimum value of free-space propagation co-efficient is selected as2.8, based on the conducted experiments.

The distance values calculated by the distance-estimating module (16)using the equation 1, are stored into a distance matrix maintained atthe distance-estimating module (16). The distance-estimating module (16)plots zone circles using the calculated distance values. Considering theaccess point as the center, zone circles are plotted using thecalculated distances as the radii of the zone circles. The plotted zonecircles are then transferred to a geometry module (22) for detectingzone of user presence. The geometry module (22) is further connected toa repository (18). The repository (18) includes the information relatedto but not limited to the location of different access points installedwithin the infrastructure, MAC ID of all the installed access points,floor map, zone boundary, dimensions of different areas on each floor.The repository (18) further holds the information regarding differentobstruction materials present in each zone of every floor, position ofthe obstruction materials and their corresponding attenuation factors.

Referring to FIG. 1 and FIG. 3, mapping of zone circles to physicalzones in accordance with the present disclosure is shown in FIG. 3. Thegeometry module (22) correlates the zone circles with the physical zonespresent in the floor map stored in the repository (18). The zone circlesare correlated with physical zones using a maximum overlap function incoordinate geometry space. The physical zone discovered with a maximumoverlap area is found to be the zone of user presence. Thus, the zone ofuser presence is recorded at the server (50).

In this manner, the physical zone of user presence is determinedautomatically using the computer-implemented system for Wi-Fi basedindoor localization (100), and thus eliminating the need of manualsupervision as required in the conventional processes.

The system (100) further includes an attenuation module (20) connectedto the repository (18) and the decision module (14). Each signal whiletraveling from an access point to the user device (80) experiencesattenuation due to various obstruction materials coming across the way.The obstruction materials may include but are not limited to differentbuilding constituents like cement, concrete, gypsum, glass and othermetal or wooden articles present on the floors. The attenuation module(20) receives from the repository (18), the information regarding theposition of the obstruction materials present in each zone of everyfloor and their corresponding attenuation factors. The attenuationmodule (20) calculates the compensated RSSI values for the zoneboundaries considering the attenuation factors. These compensated RSSIvalues are then updated to the RSSI values present at the decision treestored at the decision module (14). Thus, the attenuation module (20)leads to enrichment of the decision tree structure upon consideringphysical attenuation factors resulting from the constituents of theinfrastructure to provide a compensated decision tree. In this manner,the attenuation module (20) improves the accuracy of zone identificationand hence improves performance of the system (100).

The system (100) further takes care of dynamic obstructions like movingpersons. In case a moving person comes into the line of sight, he blocksthe signal at that point of time. The user device (80) misses a readingat time ‘t’, but receives a reading at time ‘t+Δt’, while the movingobstruction moves away from the line of sight. In this manner, thesystem takes care of dynamic obstructions by continuous learning of thereadings.

The decision module (14) communicates with the communication module (10)for transferring the compensated decision tree. The compensated decisiontree stored at the decision module (14) is transferred to the userdevice (80) through the communicating module (10). The processor (8)connected to the user memory (4) of the user device (80) receives andstores, the compensated decision tree.

During a refinement mode of operation, the user device (80) having thestored compensated decision tree travels again into a zone of theinfrastructure. The user device (80) having the Wi-Fi module (6) kept inscanning mode, starts capturing the data point values. The newlycaptured data point values include the RSSI values from different nearbylocated access points. The processor (8) of the user device (80) startsmatching the newly captured RSSI values with the compensated decisiontree holding all previously captured RSSI values tagged with zoneinformation. Upon discovering a successful match, the processor (8) ofthe user device (80) identifies the zone of user presence.

The Wi-Fi module (6) connected to the processor (8) of the user device(80) continues capturing the data point values, and transmits them tothe server (50) periodically, for improving the decision tree maintainedat the decision module (14) of the server (50). Any newly added orremoved access point is automatically detected by the system (100) uponcontinuous processing of the new RSSI values and the decision tree isupdated accordingly. The decision module (14) follows the hierarchy ofcategorization and accordingly places and stores a newly detected accesspoint at a suitable position in the decision tree. This provides thesystem (100) with self-learning ability and automatic adaptability tothe environmental changes.

Referring to FIG. 4, there is shown a flow diagram corresponding to theprocess of Wi-Fi based indoor localization. The method includes thefollowing steps:

-   -   collecting data points including RSSI values (200);    -   k-means clustering of the RSSI values (202);    -   condition based branching of RSSI clusters and decision-tree        formation (204);    -   converting the RSSI values into distance matrix and plotting        zone circles (206);    -   correlating plotted zone circles with physical zones and        detecting zone of user presence (208); and    -   generating compensated RSSI values and updating the        decision-tree (210).

In accordance with an exemplary embodiment of the present disclosure,FIG. 5 shows the initial and the refinement mode of operation of thecomputer implemented Wi-Fi based indoor localization system. The systemincludes a server (50) having preconfigured static configurationinformation. The information is related to the following factors:

-   -   1. Floor plan geometry,    -   2. Access points geometry,    -   3. Zone boundaries,    -   4. Obstruction material information including the geometry of        floor plan, type of material and thickness of the material used,        and    -   5. Attenuation factor of the obstruction materials.

During an initial mode of operation, the user device (80) moves aroundthe floor of an indoor environment and scans the Wi-Fi signals forcapturing RSSI values. There are ‘x’ zones on a floor. The user device(80) collects 100 data point values, set as the threshold limit, foreach zone of the floor. The user device (80) transmits the 100 datapoint values indicative of RSSI values to the server (50), indicated bystep A, for further processing.

The server (50) applies a k-means clustering technique on the 100 datapoint values, indicated by step B. The 100 data point values areclustered into ‘k’ groups upon application of the clustering technique.Each data point is tagged with one of the ‘k’ clusters. Value of ‘k’ isequal to ‘x’, which is the number of zones on the floor.

Each cluster of data point values is provided to decision treeclassifier, indicated by step C. The classifier generates a decisiontree using the cluster of data point values. Decision tree is a set ofrules (if-else ladder structure) providing an exact condition for theRSSI value of each data point to lie in one cluster or the other. Forexample, a node representing the rule,

if (Access_Point1_RSSI<−51 db AND Access_Point2_RSSI<−31 db): indicatescluster1,

else if (Access_Point2_RSSI<−31 db AND Access_Point4_RSSI<−41 db)):indicates cluster2, and

else: indicates cluster 3

‘AND’ used in the rules specifies the intersection.

All the rules of the decision tree are used for geometry calculation, asindicated by step D. Each individual rule is converted into a circlewith specified access point as the center of the circle and distance(converted from RSSI using equation 1) as radius of the circle.Inequality condition represents a region interior/exterior to the circlebased on the inequality and, equality condition represents periphery ofthe circle.

All the rules from the decision tree are split into individual rules andare converted to geometry. The Cluster geometry is calculated byintersecting all the geometries corresponding to individual rulesassociated with each cluster. In case of multiple conditions in thedecision tree for a particular cluster, union of the correspondinggeometries is considered as cluster geometry.

The server calculates area overlap between the clusters geometry and thestatic zone geometry, as indicated by the step E. The static zonegeometry refers to the geometry of the zone areas indicated by thestatic parameters stored at the server (50). The maximum area overlap ofthe cluster with the particular zone is used to tag the cluster withthat particular zone.

Once the zones are identified upon overlapping, the decision tree isupdated to reflect the zones, as indicated by step F. In case of maximumoverlap of cluster 1 with Zone_X, cluster2 with Zone_Y and, cluster3with Zone_Z, decision tree rules are updated as below:

if (Access_Point1_RSSI<−51 db AND Access_Point2_RSSI<−31 db): is taggedwith Zone_X ,

else if (Access_Point2_RSSI<−31 db AND Access_Point4_RSSI<−41 db)): istagged with Zone_Y, and

else: is tagged with Zone_Z.

Each condition is tagged with the corresponding zone, as shown above.

The server (50) transfers the decision tree rules with zone details tothe user device (80), as indicated by step G. The user device (80)stores the decision tree into the user memory (4). The user device (80)senses a new location data point and searches in the decision tree tolocate the user's zone of presence. In addition, the user device (80)keeps accumulating data points for further refinement.

During a refinement mode of operation, the system (100) has the decisiontree stored in the user memory (4) and is working. The major objectiveof this mode is to improve the accuracy of the system by continuouslearning. The user device (80) again collects data points up to athreshold level of 100 and transmits them to the server (50) for furtherrefinement. These data point values are processed in a manner similar tothe initial mode of operation. With the increasing number of datapoints, the decision tree is refined and improved at every level. Therefined decision tree with updated set of values is transferred to theuser device (80) for continuous improvement and providing betterresults.

Technical Advancements and Economic Significance

The technical advancements offered by the present disclosure include therealization of a computer-implemented system and method for Wi-Fi basedindoor localization having the following technical advantages:

-   -   manual supervision and explicit training is eliminated and        system works automatically;    -   self-learning is provided;    -   trilateration technique is not involved; and    -   robustness and accuracy are improved.

While considerable emphasis has been placed herein on the components andcomponent parts of the preferred embodiments, it will be appreciatedthat many embodiments can be made and that many changes can be made inthe preferred embodiments without departing from the principles of theinvention. These and other changes in the preferred embodiment as wellas other embodiments of the invention will be apparent to those skilledin the art from the disclosure herein, whereby it is to be distinctlyunderstood that the foregoing descriptive matter is to be interpretedmerely as illustrative of the invention and not as a limitation.

1. A computer implemented method for indoor localization, said methodcomprising the following: storing, in a repository, the floor plan of anindoor area including zone details, zone boundaries, building materialsand location of different access points with respect to the zones on thefloor plan; collecting a threshold number of data points from at leastone user device located in said area, wherein each of the data pointsincludes a plurality of Received Signal Strength Indicators (RSSI)captured from the access points positioned in said area; performing ak-means clustering technique on the data points for grouping the datapoints into ‘k’ number of clusters; building at least one decision treeusing the RSSIs contained by the cluster of data points, wherein thedecision tree is built by following a condition based approach, andwherein the conditions are related to the values of the RSSIs related toeach of the access points; calculating distance values pertaining to theRSSIs stored at the decision tree and plotting zone circles, wherein thezone circles are plotted using the distance values as radii of the zonecircles, the access points as centers of the respective zone circles;and determining the zone of user presence by correlating the plottedzone circles upon the floor plan using maximum overlap property.
 2. Themethod of claim 1, wherein said method comprises: generating compensatedRSSI values for the zones on the floor plan, wherein the compensatedRSSI values are generated by considering attenuation factors offered bybuilding materials of zone boundaries on the captured RSSI values; andupdating the decision tree using the compensated RSSI values.
 3. Themethod of claim 1, wherein said method further comprises: transmittingthe decision tree to a user device located in said indoor area; storingthe decision tree in the memory of the user device; capturing a currentdata point including current RSSI values; and determining the zone ofuser presence of the user device, wherein the zone is determined bycomparing the current RSSI values with the RSSI values associated withthe decision tree.
 4. A computer implemented system for indoorlocalization, said system comprising: a repository configured to storein relation to an indoor area, at least one attribute selected from thegroup consisting of a floor plan including zone details, zoneboundaries, building materials and location of different access pointswith respect to the zones on the floor plan; a communicating moduleconfigured to receive a threshold number of data points, wherein each ofthe data point includes a plurality of Received Signal StrengthIndicators (RSSI) captured from the access points positioned in saidarea; a clustering module connected to the communicating module, whereinthe clustering module is configured to perform a k-means clusteringtechnique on the data points for grouping the data points into ‘k’number of clusters; a decision module connected to the clustering moduleand the communicating module, wherein the decision module is configuredto build at least one decision tree using the RSSIs of each point in thecluster of data points, and wherein the decision tree is built byfollowing a condition based approach, and wherein the conditions arerelated to the values of the RSSIs related to each of the access point;a distance-estimating module connected to the decision module, whereinthe distance-estimating module is configured to calculate distancevalues pertaining to the RSSIs stored at the decision tree and plottingzone circles, and wherein the zone circles are plotted using thedistance values as radii of the zone circles and the access points ascenters of the respective zone circles; and a geometry module connectedto the distance-estimating module and the repository, wherein thegeometry module is configured to determine the zone of user presence bycorrelating the plotted zone circles upon the floor plan using maximumoverlap property.
 5. The system of claim 4, further comprising, anattenuation module connected to: the repository for generatingcompensated RSSI values for the zones of the floor, wherein thecompensated RSSI values are generated by considering attenuation factorsoffered by building materials of zone boundaries on captured RSSIvalues; and the decision module for updating the decision tree using thecompensated RSSI values, wherein the compensated decision tree iscommunicated to the communicating module.
 6. The system of claim 4,further comprising, a user device comprising: a Wi-Fi module adapted forcontinuously recording current RSSI values of data points captured fromthe access points; a user memory adapted to store the data point valuesrecorded by the Wi-Fi module and the decision tree received from thecommunicating module; and a processor adapted to receive from thecommunicating module, the information regarding the zone of userpresence, and further determine the current zone in which user ispresent, wherein the zone is determined by comparing the current RSSIvalues with the RSSI values pertaining to the decision tree.